Instructions to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mrkwanzaa/functionalizer-100M-github-code-python-seed1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed1")
model = AutoModelForCausalLM.from_pretrained("mrkwanzaa/functionalizer-100M-github-code-python-seed1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrkwanzaa/functionalizer-100M-github-code-python-seed1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkwanzaa/functionalizer-100M-github-code-python-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed1

SGLang

How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mrkwanzaa/functionalizer-100M-github-code-python-seed1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkwanzaa/functionalizer-100M-github-code-python-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mrkwanzaa/functionalizer-100M-github-code-python-seed1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrkwanzaa/functionalizer-100M-github-code-python-seed1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mrkwanzaa/functionalizer-100M-github-code-python-seed1 with Docker Model Runner:
```
docker model run hf.co/mrkwanzaa/functionalizer-100M-github-code-python-seed1
```

Fix metadata, add pipeline tag and library name

by nielsr HF Staff - opened 1 day ago

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

-4

nielsr

1 day ago

This PR improves the model card's metadata by:

removing the arxiv field from the YAML metadata, since it belongs in the markdown content rather than the metadata
adding pipeline_tag: text-generation so the model can be found under the text-generation pipeline
adding library_name: transformers to enable the automated Transformers usage snippet
linking to the paper on the Hugging Face Papers page

Fix metadata, add pipeline tag and library name677cd25d

mrkwanzaa changed pull request status to merged about 18 hours ago

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